Data for figures
Description
This dataset compiles original data from a study investigating microbial iron reduction in sediments of Tibetan Plateau lakes with varying salinity (e.g., Erhai Lake, Tuosu Lake). The research hypothesis posits that sediment physicochemical properties (e.g., salinity, pH, total organic carbon) significantly influence microbial iron reduction kinetics and mineralogical transformations, driving biogeochemical cycles in extreme environments. The data includes: 1) Sediment physicochemical characteristics: Measurements of salinity, pH, TOC, TN, TP, and ion concentrations from lake sediments. 2) Iron reduction kinetics: Time-course data on Fe(II) production during anaerobic incubations, reflecting microbial activity. 3) Mineralogical analysis: X-ray diffractograms (XRD) identifying iron oxides (e.g., goethite, hematite) and other minerals. Key findings reveal that high-salinity lakes (e.g., KeKe Lake) exhibit suppressed iron reduction rates, indicating salinity inhibition of microbial processes. Iron reduction is coupled with sulfur cycling, as evidenced by XRD-based mineral transformations. These insights highlight salinity as a critical regulator of iron biogeochemistry. Data were gathered through field sediment sampling, laboratory anaerobic incubations with spectrophotometric Fe(II) analysis, and XRD mineral identification. The data can be interpreted using kinetic models (e.g., first-order fitting) to quantify reduction rates and correlated with environmental parameters to assess drivers of iron cycling. Researchers may use this dataset for comparative studies on saline lake biogeochemistry, modeling efforts, and understanding microbial-mineral interactions.
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Steps to reproduce
This dataset supports a study on microbial iron reduction in sediments from saline lakes on the Tibetan Plateau (e.g., Erhai Lake, Tuosu Lake). The research hypothesis is that sediment physicochemical properties (e.g., salinity, pH, total organic carbon) control microbial iron reduction rates and pathways, influencing mineral transformations and biogeochemical cycles in extreme environments. The data demonstrate how environmental factors drive iron cycling, with notable findings showing that high salinity (e.g., in KeKe Lake) suppresses microbial activity, reducing iron reduction kinetics, and that iron reduction is coupled with sulfur cycling, as evidenced by mineralogical changes. Data Collection Steps to Reproduce: 1)Sediment Sampling: Surface sediments (0-10 cm depth) were collected from multiple lakes using a grab sampler, stored anaerobically at 4°C, and transported to the laboratory. 2)Physicochemical Analysis: Sediments were analyzed for salinity (via conductivity), pH (electrode method), TOC/TN (elemental analyzer), TP (spectrophotometry after digestion), and major ions (ion chromatography). 3)Iron Reduction Kinetics: Anaerobic microcosms were set up with sediments and artificial lake water, incubated at 25°C. Fe(II) production was monitored over time (0-40 days) using the ferrozine method with UV-Vis spectrophotometry. 4)Mineralogical Analysis: Sediment samples were freeze-dried, ground, and analyzed by X-ray diffraction (XRD) with Cu-Kα radiation to identify iron oxides (e.g., goethite, hematite) and other minerals. Interpretation and Use: The data can be interpreted by fitting kinetic models (e.g., first-order kinetics) to Fe(II) production curves, correlating rates with physicochemical parameters. Researchers can use this dataset to compare iron reduction across salinity gradients, model biogeochemical processes, or study microbial-mineral interactions in saline environments. The methods ensure reproducibility for similar studies on aquatic sediments.
Institutions
- Henan University